Definition: Techniques used to prevent overfitting by adding a penalty term to the objective function based on the complexity of the model.
Better definition: When your computer goes on a complexity diet to keep overfitting at bay.
Where does this fit in the AI Landscape?
Regularization plays an important role in building robust AI models by preventing overfitting and improving generalization. It's a fundamental technique in machine learning and is widely used across various applications and industries, ensuring the reliability and effectiveness of AI systems.
What are the real world impacts of this?
Regularization techniques prevent overfitting in machine learning models, making the predictions of these models more reliable and generalizable. This leads to more accurate and trustworthy AI technologies in various sectors, from finance to healthcare. For developers, regularization is a fundamental technique for improving the robustness and performance of machine learning models.
What could go wrong in the real world with this?
A model with regularization is tasked with designing a complex roller coaster but opts for a minimalistic approach, creating a ride that consists of a single, gently sloping hill.
A technique to reduce overfitting. Could be used during model training to ensure the model generalizes well to new, unseen code or chat messages.